Can AI be added to an existing product?

Often yes, if APIs and data access exist. We start by picking a suitable scenario.

Do you integrate ChatGPT / Claude / Gemini?

Yes. Provider choice depends on cost, latency, data policy and language quality.

How is company data used?

We define what stays private, what is sent to a model, and what is logged before build starts.

What is RAG and when is it needed?

RAG means the model finds relevant company documents before answering, so responses are grounded in your data rather than only general model knowledge.

How do you handle incorrect AI output?

Human approval for critical actions, citations, limited tool/action access and evaluation sets. We do not automate everything blindly.

Can we start with a PoC?

Yes. A narrow PoC is the safest way to avoid a wrong investment.

How is usage cost calculated?

Model calls are token/request based. We estimate in PoC and set limits plus monitoring in production.

AI Integration

AI Integration Embedded in Real Workflows

“Add AI” is not a strategy. The goal is to shorten a repeated task or give your product a measurable AI capability.

We integrate model APIs from providers such as OpenAI, Anthropic and Google, build source-backed search over your documents, extract structured data and ship in-product assistants.

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AI layer on existing products
RAG and document processing
PoC-first delivery

When do you need this?

AI should not be glued onto everything. These are useful signals.

The same documents are re-read

Quotes, contracts, reports or tickets are processed by hand again and again.

Support answers are written from scratch

Teams retype the same answers while knowledge stays fragmented.

You need structure from unstructured text

PDF, email and notes should become searchable, usable fields.

An in-product assistant is required

Users should ask questions or start actions inside your product.

Source-backed search over company data

You want answers grounded in authorized company data, not only general model knowledge.

A routine workflow can be automated

Classification, summarization, routing or draft generation can remove a repeated step.

What we build

Product integration, not a research lab pitch.

In-product AI assistants

Helpers embedded in your web or mobile product.

RAG search

Source-backed answers over company documents and data.

Document extraction

Structured fields from PDFs, forms and tickets.

Workflow automation

Summarize, label, route and draft to speed operations.

Chat and support layers

Bots with clear boundaries for web or internal tools.

Model API integrations

Controlled use of OpenAI, Anthropic, Google and similar providers.

How we work

PoC first. Production second. Cost control always.

01
Define the job
Which task shrinks, how success is measured, which data is allowed.
02
PoC / pilot
A narrow scenario tests model + data + UI. Stopping is a valid outcome.
03
Product integration
Wire API, permissions, logs and UX into the existing app.
04
Evaluation and guardrails
Incorrect or invented model output, personal data, permissions and human-approval points are defined.
05
Live monitoring
Usage cost, quality samples and iteration on prompts/data/models.

Delivery scope

Typical AI engagement items.

Use case and success criteria
Data sources and access model
Model / provider selection
RAG or tool design
Product UI integration
Prompt and evaluation set
Logging and cost monitoring
PoC to production handoff

Technical approach

Data and business rules first, then the model. Stack follows your product.

Model APIs

Controlled integrations with OpenAI, Anthropic, Gemini and similar providers.

RAG

Chunking, embeddings, retrieval and source-backed answers.

Application layer

API and UI hooks into existing Next.js / Django / Node products.

Observability

Request logs, token/cost tracking and quality sampling.

FAQ

Frequently Asked Questions

Start with the scenario

Tell us which task you want to shorten. We will check whether a PoC makes sense.

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